Diagnostic support apparatus and diagnostic support method

ABSTRACT

A diagnostic support apparatus includes a base vector matching unit configured to match test image base vectors used for a base representation of a test image feature quantity of a test image and normal image base vectors used for a base representation of a normal image feature quantity of a normal image, a lesion determination unit configured to determine that the test image includes an image of a lesion site when a difference between a test image base coefficient and a normal image base coefficient is greater than a threshold, the test image base coefficient being a coefficient with which the test feature quantity is transformed to the base representation, and the normal image base coefficient being a coefficient with which the normal feature quantity is transformed to the base representation, and a determination result output unit configured to output a result of the determination by the lesion determination unit.

CROSS REFERENCE TO RELATED APPLICATION

The present application is based on and claims priority of JapanesePatent Application No. 2012-270449 filed on Dec. 11, 2012. The entiredisclosure of the above-identified application, including thespecification, drawings and claims is incorporated herein by referencein its entirety.

FIELD

One or more exemplary embodiments disclosed herein relate generally to adiagnostic support apparatus and method that support image-baseddiagnosis by doctors.

BACKGROUND

In order to support image-based diagnosis by doctors, there haveconventionally been proposed apparatuses in which a test image, which isa medical image of a subject to be tested, and normal images, which aremedical images of normal structures, are each represented by shapevectors, and the shape vectors of the test image are compared with theshape vectors of the normal images so as to determine the presence orabsence of a lesion site (see Patent Literature 1, for example).

CITATION LIST Patent Literature [PTL 1]

-   Japanese Unexamined Patent Application Publication No. 2004-41694

SUMMARY Technical Problem

With the conventional apparatuses, however, if different methods areused to describe or calculate the shape vectors of normal images and theshape vectors of a test image, it is impossible to accurately determinethe presence or absence of a lesion site by simply making a comparisonbetween the shape vectors of the test image and the shape vectors of thenormal images. Similar situations can also arise when base vectors usedas a basis to represent the image feature quantities of normal imagesare different from base vectors used as a basis to represent the imagefeature quantities of a test image.

One or more non-limiting and exemplary embodiments disclosed hereinprovide a diagnostic support apparatus and method that make it possibleto accurately determine the presence or absence of a lesion site withoutdepending on the method for describing or calculating image featurequantities typified by shape vectors.

Solution to Problem

In one general aspect, the techniques disclosed here feature adiagnostic support apparatus that includes a base vector matching unitconfigured to match base vectors and base vectors, the base vectorsbeing different from the base vectors, the base vectors being used as abasis to represent a test feature quantity that is an image featurequantity of a test image in which presence of an image of a lesion siteis unknown, and the base vectors being used as a basis to represent anormal feature quantity that is an image feature quantity of a normalimage that does not include an image of a lesion site, a lesiondetermination unit configured to determine that the test image includesan image of a lesion site when a difference between a coefficient and acoefficient is greater than a determination threshold value, thecoefficient being a coefficient with which the test feature quantity istransformed to a base representation, and the coefficient being acoefficient with which the normal feature quantity is transformed to abase representation, and a determination result output unit configuredto output a result of the determination by the lesion determinationunit.

With this configuration, the base vector matching unit matches the basevectors of the test feature quantity and the base vectors of the normalfeature quantity. This allows the diagnostic support apparatus tocompare the test feature quantity and the normal feature quantity thatcannot be compared as they are due to their different base vectors.Accordingly, it is possible to accurately determine the presence orabsence of a lesion site without depending on the method for describingor calculating image feature quantities.

The general and specific aspect disclosed above may be implemented usinga system, a method, an integrated circuit, a computer program, acomputer-readable recording medium such as a CD-ROM, or any combinationthereof.

Additional benefits and advantages of embodiments to be disclosed willbe apparent from the Specification and Drawings. The benefits and/oradvantages may be individually obtained by various embodiments andfeatures of the Specification and Drawings, which need not all beprovided in order to obtain one or more of such benefits and/oradvantages.

Advantageous Effects

The diagnostic support apparatus and method according to one or moreexemplary embodiments or features disclosed herein make it possible toaccurately determine the presence or absence of a lesion site withoutdepending on the method for describing or calculating image featurequantities.

BRIEF DESCRIPTION OF DRAWINGS

These and other advantages and features will become apparent from thefollowing description thereof taken in conjunction with the accompanyingDrawings, by way of non-limiting examples of embodiments disclosedherein.

FIG. 1 illustrates a method for setting landmarks disclosed in PTL 1.

FIG. 2 shows an example of the method for calculating shape vectors.

FIG. 3 is a block diagram showing a functional configuration of adiagnostic support apparatus according to one exemplary embodiment.

FIG. 4 is a block diagram showing functional configurations of adiagnostic support apparatus and an image server according to Embodiment1.

FIG. 5 illustrates image-feature-quantity management numbers.

FIG. 6 illustrates a method for calculating base vectors and averagevectors for normal structures.

FIG. 7 shows an example of calculation of an image feature quantityvector through wavelet transformation.

FIG. 8 shows an example of calculation of wavelet coefficients usingHaar mother wavelet transform.

FIG. 9 illustrates the relationships between the directions of basevectors and base coefficient vectors.

FIG. 10 illustrates the directions of base vectors.

FIG. 11 shows an example of the method for determining a determinationthreshold value for use in determining the presence or absence of alesion.

FIG. 12 is a flowchart of processing performed by the diagnostic supportapparatus according to Embodiment 1.

FIG. 13 is a block diagram showing a functional configuration of adiagnostic support apparatus according to Embodiment 2.

FIG. 14 is a block diagram showing a functional configuration of adiagnostic support apparatus according to Embodiment 3.

FIG. 15 is a block diagram showing a functional configuration of adiagnostic support apparatus according to Embodiment 4.

FIG. 16 is a block diagram showing a functional configuration of adiagnostic support apparatus according to Embodiment 5.

FIG. 17 is a block diagram showing a functional configuration of adiagnostic support apparatus according to Embodiment 6.

FIG. 18 illustrates an exemplary method for transmitting base vectors.

FIG. 19 is a block diagram showing a functional configuration of adiagnostic support apparatus capable of updating the base vectors fornormal images.

DESCRIPTION OF EMBODIMENTS Underlying Knowledge Forming Basis of thePresent Disclosure

In relation to the conventional apparatuses disclosed in the Backgroundsection, the inventors have found the following problem:

Digitization of medical images improves consistency between the medicalimages and data processing by computers and is increasing opportunitiesfor IT systems to support diagnostic practices by doctors andtechnicians. One example is computer-aided detection (CAD), which is amethod of utilizing computers for detection of a lesion site.

Diagnosticians usually remember medical images of normal structures thatinclude no lesion sites. When a medical image of a subject to be tested,namely a test image, is presented, a diagnostician thinks of medicalimages of normal structures, namely normal images, and compares the testimage with the normal images. When having found a difference between thetest image and the normal images, the diagnostician determines that thearea of difference is a lesion site. In computer processing, calculatinga difference between two pieces of data is a basic function. Thus,image-based diagnosis in which a lesion area is detected by thecomparison between a test image and normal images is what the computerprocessing is good at.

Such difference calculations, however, require alignment between a testimage and normal images. To generate the normal images, usually pastmedical images are used. Specifically, when a diagnostician checks atest image and finds no lesion site in it, the test image is regarded asa normal image. If past test images for a patient include no lesionsite, these test images can be used as normal images. However, positionsin a test image and corresponding positions in normal images usually donot match even for the same patient due to various factors such asdifferent shooting conditions or changes in the patient's shape. Inaddition, normal images cannot be obtained when a test image is capturedfor the first time, because there are no images to be compared. In thiscase, normal images for other patients are used, but alignment between atest image and the normal images is necessary due to a difference inshape from the other patients.

Incidentally, normal images are generated from images that were capturedin the past and have already been checked that no lesions are included.One reason for this is, as described above, that a test image for apatient captured for the first time has no medical images to becompared. The other reason is that medical knowledge tends to be builtup by the accumulation of knowledge from past cases and it is morelikely that generating normal images having no lesions from past caseswill have higher medical utility values. The medical knowledge is makingsteady improvement, which often improves the interpretation of pastcases. Therefore, the medical knowledge registered in IT systems alwaysneeds updating, and even normal images are no exceptions.

In view of this, it is desirable to collect normal images for aplurality of patients and generate highly versatile normal images thatcan comprehensively represent these collected images. One specificexample of implementing such generation is representing a normal imageas a linear combination of an average shape and an eigen shape asdisclosed in PTL 1. In other words, a shape vector x representing anormal image is expressed using Expression 1 below.

x=x _(ave) +Ps·bs  [Expression 1]

Here, x_(ave) is the average shape vector, Ps is the eigen shape vector,and bs is a set of shape coefficients.

The average shape vector x_(ave) and the eigen shape vector Ps arenecessary for the calculation of Expression 1, and landmarks M as shownin FIG. 1 are set in an image to vectorize image information. Thelandmarks M are indicated by black dots in FIG. 1. The x and ycoordinates of the landmarks M serve as vector elements and are used tovectorize image information. As shown in test images P1, P2, and P3,landmarks are individually set in a plurality of normal images, a shapevector is defined, and the landmarks and the shape vector are used tocalculate an average vector and an eigen vector. Note that a test imagecan also be expressed in a similar manner, using Expression 1.

Using the vectors described above, a test image and normal images arealigned and a lesion site is detected from a difference between the testimage and the normal images. This supports image-based diagnosis.

With the conventional technique, however, if different methods are usedto represent shape vectors between a test image and normal images,compatibility with the normal images expressed using Expression 1 willbe lost. For example, if the number of landmarks differs between a testimage and normal images, the number of dimensions of shape vectors willdiffer. Here, shape vectors can be represented using differentcalculation methods other than the calculation method using landmarks.For example, it is possible, as shown in FIG. 2, to represent a shapevector as a 9-dimensional vector consisting of differences in pixelvalues between a center pixel and each of the eight pixels adjacent tothe center pixel. In FIG. 2, if dx (x=1 to 9) denotes the pixel value,the aforementioned 9-dimensional vector can be represented as (d5,d5-d1, d5-d2, d5-d3, d5-d4, d5-d6, d5-d7, d5-d8, d5-d9). Shape vectorsrepresented using such a method are different from shape vectorsconsisting of the x and y coordinates of landmarks. It is alsoconceivable to calculate shape vectors using widely available encodingsystems such as wavelet transformation.

One or more exemplary embodiments disclosed herein provide a diagnosticsupport apparatus and method that make it possible to accuratelydetermine the presence or absence of a lesion site even if differentmethods are used to describe or calculate image feature quantitiestypified by shape vectors.

According to an exemplary embodiment disclosed herein, the diagnosticsupport apparatus includes a base vector matching unit configured tomatch base vectors and base vectors, the base vectors being differentfrom the base vectors, the base vectors being used as a basis torepresent a test feature quantity that is an image feature quantity of atest image in which presence of an image of a lesion site is unknown,and the base vectors being used as a basis to represent a normal featurequantity that is an image feature quantity of a normal image that doesnot include an image of a lesion site, a lesion determination unitconfigured to determine that the test image includes an image of alesion site when a difference between a coefficient and a coefficient isgreater than a determination threshold value, the coefficient being acoefficient with which the test feature quantity is transformed to abase representation, and the coefficient being a coefficient with whichthe normal feature quantity is transformed to a base representation, anda determination result output unit configured to output a result of thedetermination by the lesion determination unit.

FIG. 3 is a block diagram showing a functional configuration of thediagnostic support apparatus. The diagnostic support apparatus includesa base vector matching unit 1901, a lesion determination unit 107, and adetermination result output unit 109.

With this configuration, the base vector matching unit matches the basevectors of a test feature quantity and the base vectors of a normalfeature quantity. This allows the diagnostic support apparatus tocompare the test feature quantity and the normal feature quantity thatcannot be compared as they are due to their different base vectors.Accordingly, it is possible to accurately determine the presence orabsence of a lesion site without depending on the method for describingor calculating image feature quantities.

For example, the base vector matching unit may include a base vectortransformation unit configured to, when the test feature quantity andthe normal feature quantity match in type, perform transformationprocessing for matching the base vectors used as a basis to representthe test feature quantity to the base vectors used as a basis torepresent the normal feature quantity, and represent the test featurequantity as a linear combination of the base vectors of the normalfeature quantity with the coefficient, and the lesion determination unitmay be configured to determine that the test image includes an image ofa lesion site when the difference between the coefficient with which thenormal feature quantity is transformed to a base representation and thecoefficient used with the test image base vectors that have undergonethe transformation processing is greater than the determinationthreshold value.

With this configuration, the transformation processing is performed tomatch the base vectors of the test feature quantity to the base vectorsof the normal feature quantity. This allows the diagnostic supportapparatus to compare the test feature quantity and the normal featurequantity that cannot be compared as they are due to different methodsfor describing image feature quantities. Accordingly, it is possible toaccurately determine the presence or absence of a lesion site withoutdepending on the method for describing image feature quantities.

For example, the diagnostic support apparatus may further include anormal coefficient storage unit configured to store at least one of thecoefficient with which the normal feature quantity is transformed to abase representation, wherein the lesion determination unit is configuredto determine that the test image includes an image of a lesion site whena difference between a selected one of the at least one coefficientstored in the normal coefficient storage unit and the coefficient usedwith the test image base vectors that have undergone the transformationprocessing is greater than the determination threshold value.

The base vector transformation unit may be further configured to receivea first coefficient from an external apparatus, the first coefficientbeing a coefficient with which an image feature quantity of a normalimage is transformed to a base representation, the image featurequantity being of the same type as the normal feature quantity, toperform transformation processing for matching first base vectorscorresponding to the first coefficient with second base vectorscorresponding to the at least one coefficient stored in the normalcoefficient storage unit, to represent the image feature quantitycorresponding to the first coefficient as a linear combination of thesecond base vectors with a second coefficient, and to add the secondcoefficient into the normal coefficient storage unit.

With this configuration, the transformation processing for matching basevectors is performed even if the received coefficient corresponds to anormal image represented by base vectors that are different from thebase vectors corresponding to the coefficient stored in the normalcoefficient storage unit. Accordingly, it is possible to addcoefficients corresponding to image feature quantities of normal imagesinto the normal coefficient storage unit without depending on the methodfor describing image feature quantities.

The diagnostic support apparatus may further include a data receptionunit configured to receive first data and second data at different timesand combine the received first data and the received second data torestore the base vectors used as a basis to represent the test featurequantity, the first data and the second data being obtained by dividingthe base vectors of the test feature quantity.

With this configuration, even if the third party could acquire eitherthe first data or the second data by interception or the like, it isimpossible for the third party to restore the base vectors from theacquired first or second data. Thus, even if the third party couldacquire a coefficient corresponding to a test feature quantity, thethird party cannot restore the test image without having the basevectors. This provides information security.

The diagnostic support apparatus may further include a nearest neighbordetection monitoring unit configured to output an instruction to updatethe base vectors of the normal feature quantity when the differencebetween the coefficient with which the normal feature quantity istransformed to a base representation and the coefficient with which thetest feature quantity is transformed to a base representation is greaterthan a reference value.

With this configuration, it is possible to update the base vectors ofnormal feature quantities when input of an unexpected test imageproduces a considerable difference between a test feature quantity and anormal feature quantity.

The base vector matching unit may include a pixel value transformationunit configured to, when the test feature quantity and the normalfeature quantity do not match in type, transform the test featurequantity to a pixel value to restore the test image, an image featurequantity calculation unit configured to calculate an image featurequantity of the same type as the normal feature quantity from therestored test image, and a base representation unit configured torepresent the image feature quantity calculated by the image featurequantity calculation unit as a linear combination of the base vectors,with which the normal feature quantity is transformed to a baserepresentation, with a coefficient. The lesion determination unit may beconfigured to determine that the test image includes an image of alesion site when a difference between the coefficient with which thenormal feature quantity is transformed to a base representation and thecoefficient used by the base representation unit to represent thecalculated image feature quantity is greater than the determinationthreshold value.

With this configuration, when different methods for calculating imagefeature quantities are used between a test image and normal images, atest image is restored from the image feature quantities of the testimage, and then new image feature quantities are calculated from therestored image using the same calculation method as that used tocalculate the normal feature quantities. Accordingly, it is possible todetermine the presence or absence of a lesion site without depending onthe method for calculating image feature quantities.

For example, the diagnostic support apparatus may further include anormal coefficient storage unit configured to store at least one of thecoefficient with which the normal feature quantity is transformed to abase representation, wherein the pixel value transformation unit isfurther configured to receive an image feature quantity of a normalimage from an external apparatus and transform the received imagefeature quantity to a pixel value to restore the normal image, the imagefeature quantity being of a different type from the normal featurequantity, the image feature quantity calculation unit is furtherconfigured to calculate an image feature quantity of the same type asthe normal feature quantity from the restored normal image, and the baserepresentation unit is further configured to represent the image featurequantity calculated by the image feature quantity calculation unit as alinear combination of the base vectors, with which the normal featurequantity is transformed to a base representation, with a coefficient,and to add the coefficient into the normal coefficient storage unit.

With this configuration, even if the received image feature quantitiesof a normal image have been calculated using a different method fromthat used to calculate the normal feature quantities corresponding tothe coefficients stored in the normal coefficient storage unit, thenormal image can be restored from the received image feature quantities,and then new image feature quantities are calculated from the restorednormal image using the same method as that used to calculate the normalfeature quantities. Accordingly, it is possible to add coefficients ofthe image feature quantities of normal images into the normalcoefficient storage unit without depending on the method for calculatingimage feature quantities.

The diagnostic support apparatus may further include an image featurequantity transformation unit configured to transform the coefficient,with which the normal feature quantity is transformed to a baserepresentation, to the normal feature quantity, wherein the baserepresentation unit is configured to calculate base vectors from thenormal feature quantity transformed by the image feature quantitytransformation unit and the image feature quantity calculated by theimage feature quantity calculation unit, and to represent each of thetransformed normal feature quantity and the calculated image featurequantity as a linear combination of the calculated base vectors with acoefficient.

With this configuration, when different methods for calculating imagefeature quantities are used between a test image and normal images, thetest image is restored from the image feature quantities of the testimage, and new image feature quantities are calculated from the restoredimage using the same method as that used to calculate the normal featurequantities. Also, each of the normal feature quantities and the imagefeature quantities of the restored test image can be represented usingcoefficients of the base vectors calculated from the normal featurequantities and the image feature quantities of the restored test image.This allows the diagnostic support apparatus to compare the imagefeature quantities of a test image and the image feature quantities ofnormal images. Accordingly, it is possible to accurately determine thepresence or absence of a lesion site without depending on the method forcalculating image feature quantities.

The diagnostic support apparatus may further include a normalcoefficient storage unit configured to store at least one of thecoefficient with which the normal feature quantity is transformed to abase representation, wherein the pixel value transformation unit isfurther configured to receive an image feature quantity of a normalimage from an external apparatus and transform the received imagefeature quantity to a pixel value to restore the normal image, the imagefeature quantity being of a different type from the normal featurequantity, the image feature quantity calculation unit is furtherconfigured to calculate an image feature quantity of the same type asthe normal feature quantity from the restored normal image, and the baserepresentation unit is further configured to calculate base vectors fromthe normal feature quantity transformed by the image feature quantitytransformation unit and the image feature quantity calculated by theimage feature quantity calculation unit, to represent the calculatedimage feature quantity as a linear combination of the calculated basevectors with a coefficient, and to add the coefficient into the normalcoefficient storage unit.

With this configuration, even if the received image feature quantitiesof a normal image have been calculated using a different method fromthat used to calculate the normal feature quantities corresponding tothe coefficients stored in the normal coefficient storage unit, thenormal image can be restored from the received image feature quantities,and new image feature quantities are calculated from the restored normalimage using the same method as that used to calculate the normal featurequantities. Also, each of the normal feature quantities and the imagefeature quantities of the restored normal images can be representedusing coefficients of the base vectors calculated from the normalfeature quantities and the image feature quantities of the restorednormal images. Accordingly, it is possible to add coefficients of imagefeature quantities of normal images into the normal coefficient storageunit without depending on the method for calculating image featurequantities.

These general and specific aspects may be implemented using a system, amethod, an integrated circuit, a computer program, a computer-readablerecording medium such as a CD-ROM, or any combination thereof.

Hereinafter, exemplary embodiments are described in more detail withreference to the accompanying drawings.

Exemplary embodiments described below show general or specific examples.The numerical values, shapes, materials, structural elements, thearrangement and connection of the structural elements, steps, theprocessing order of the steps etc. shown in the following exemplaryembodiments are merely examples and do not limit the scope of theappended Claims and their equivalents. Therefore, among the structuralelements in the following exemplary embodiments, structural elementsthat are not recited in any one of the independent claims are describedas arbitrary structural elements.

Embodiment 1

The present embodiment describes a diagnostic support apparatus thatshares image-feature-quantity management numbers with a plurality offacilities, the image-feature-quantity management numbers being numbersfor identifying methods for calculating image feature quantities, thusmaking it possible to detect a lesion site without depending ondifferent methods for calculating image feature quantities.

FIG. 4 is a block diagram showing functional configurations of adiagnostic support apparatus 100 and an image server 150 according toEmbodiment 1.

The image server 150 is configured to transmit test data 151 for testinga patient and an image-feature-quantity management number 152 to thediagnostic support apparatus 100. The test data 151 includes imagefeature quantities of a test image for the patient. The test data 151also includes base vectors used as a basis to represent the test image,and base coefficient vectors. The image-feature-quantity managementnumber 152 is the number to identify the method used to calculate theimage feature quantities of the test image.

The diagnostic support apparatus 100 is configured to receive the testdata 151 and the image-feature-quantity management number 152 from animage server 150, which is installed in a facility different from thefacility in which the diagnostic support apparatus 100 is installed, andto detect a lesion site on the basis of the test data 151, the lesionsite being an area of difference between the test image and normalimages.

The diagnostic support apparatus 100 includes a communication controlunit 101, a data reception unit 102, an image feature quantitycomparison unit 103, a normal coefficient storage unit 104, a basevector transformation unit 105, a nearest neighbor vector detection unit106, the lesion determination unit 107, a threshold value memory unit108, the determination result output unit 109, and a determinationresult display unit 110.

The image server 150 includes a communication control unit 153 and adata transmission unit 154.

The communication control unit 101 is configured to notify the imageserver 150 that the diagnostic support apparatus 100 will receive thetest data 151 and the image-feature-quantity management number 152 fromthe image server 150. The communication control unit 101 is alsoconfigured to notify the data reception unit 102 that the data receptionunit 102 will receive the test data 151 and the image-feature-quantitymanagement number 152.

The image-feature-quantity management number 152 is assigned to eachmethod for calculating image feature quantities for convenience as shownin FIG. 5. The diagnostic support apparatus 100 and the image server 150use common image-feature-quantity management numbers 152.

When the image-feature-quantity management number 152 is 1, the methodfor calculating image feature quantities is wavelet transformation usingthe Haar kernel. The number of layers of the wavelet transformation isthree.

When the image-feature-quantity management number 152 is 2, the methodfor calculating image feature quantities is wavelet transformation usingthe Mexican Hat kernel. The number of layers of the wavelettransformation is two.

When the image-feature-quantity management number 152 is 3, the methodfor calculating image feature quantities is scale-invariant featuretransform (SIFT).

When the image-feature-quantity management number 152 is 4, the methodfor calculating image feature quantities is the operation of calculatingdifferences between a pixel of interest and pixels adjacent to the pixelof interest.

When the image-feature-quantity management number 152 is 5, the imagefeature quantities are represented by the image coordinates of aplurality of landmarks as in PTL 1.

Note that although, in this exemplary operation, the communicationcontrol unit 153 of the image server 150 instructs the data transmissionunit 154 to transmit the test data 151 and the image-feature-quantitymanagement number 152, one or more exemplary embodiments disclosedherein are not intended to limit the configuration and operations of theimage server 150.

The data reception unit 102 is configured to, upon receipt of the testdata 151 and the image-feature-quantity management number 152 from theimage server 150, transfer the test data 151 to the base vectortransformation unit 105 and transfer the image-feature-quantitymanagement number 152 to the image feature quantity comparison unit 103.

The image feature quantity comparison unit 103 is configured to hold theimage-feature-quantity management number for the image featurequantities (coefficients) of normal images stored in the normalcoefficient storage unit 104 and to compare the image-feature-quantitymanagement number 152 received from the image server 150 with the storedimage-feature-quantity management number for matching. When theimage-feature-quantity management number 152 does not match the storedimage-feature-quantity management number, the image feature quantitycomparison unit 103 notifies the communication control unit 101 that theimage-feature-quantity management numbers do not match. In response tothis, the communication control unit 101 requests the image server 150to stop the transmission of data.

The normal coefficient storage unit 104 is configured to hold basecoefficient vectors of normal images.

The base coefficient vectors of normal images are represented as amatrix by the following expression:

$\begin{matrix}\begin{matrix}{\alpha_{p} =  {B^{- 1}( {f_{p} - g} )}\Leftrightarrow\begin{pmatrix}\alpha_{p,1} \\\alpha_{p,2} \\\vdots \\\alpha_{p,n}\end{pmatrix} } \\{=  {\begin{pmatrix}b_{1} & b_{2} & \ldots & b_{n}\end{pmatrix}^{- 1}( {f_{p} - g} )}\Leftrightarrow\begin{pmatrix}\alpha_{p,1} \\\alpha_{p,2} \\\vdots \\\alpha_{p,n}\end{pmatrix} } \\{= {\begin{pmatrix}b_{1,1} & b_{2,1} & \ldots & b_{n,1} \\b_{1,2} & b_{2,2} & \ldots & b_{n,2} \\\vdots & \vdots & \vdots & \vdots \\b_{1,n} & b_{2,n} & \ldots & b_{n,n}\end{pmatrix}^{- 1}\begin{pmatrix}{f_{p{.1}} - g_{1}} \\{f_{p{.2}} - g_{2}} \\\vdots \\{f_{p.n} - g_{n}}\end{pmatrix}}}\end{matrix} & \lbrack {{Expression}\mspace{14mu} 2} \rbrack\end{matrix}$

Here, the matrix B represents a normal structural base vector matrix,and the vector g represents an average normal structural vector, both ofwhich will be described later. Expression 2 is obtained by solvingExpression 1 for a shape coefficient set bs. The correspondence betweenExpression 1 and Expression 2 is as follows:

Shape Vector x

Normal Image Feature Quantity Vector f_(p)

Average Shape Vector x_(ave)

Average Normal Structural Vector g

Eigen Shape Vector Ps

Normal Structural Base Vector Matrix B

Shape Coefficient Set bs

Normal Image Base Coefficient Vector α_(p)

The normal structural base vector matrix B and the average normalstructural vector g are calculated on the basis of the image featurequantity vectors calculated from a large number of normal images asshown in FIG. 6. If, for example, W is the width of normal images and His the height of the normal images, (W×H) image feature quantity vectorsare calculated from a single normal image. Assuming that Q is the numberof normal images, (W×H×Q) image feature quantity vectors are obtainedfrom Q normal images. The number of dimensions of each image featurequantity vector is assumed to be n.

The image feature quantities are calculated through wavelettransformation, for example.

FIG. 7 shows multiresolution representation of an image in t scalesthrough the wavelet transformation. In scale 1, differences in luminancebetween a pixel of interest and pixels adjacent to the pixel of interestare calculated, and smoothing is performed for every given number ofpixels at the time of transition to scale 2. In scale 2, differences inluminance between a pixel of interest and pixels adjacent to the pixelof interest are also calculated. It is noted here that each pixel ofscale 2 is obtained by smoothing a plurality of pixels of scale 1 andaccordingly has a lower frequency component. Therefore, carrying out thecalculations from scale 1 to scale t (t is an integer of 2 or more)yields wavelet coefficients V, H, and D of each scale with gradualtransition from high frequency components to low frequency components.Each image feature quantity vector consists of the wavelet coefficientsV, H, and D calculated in each scale and an average luminance value Lcalculated from the image of scale t, and thus has (3 t+1) dimensions.

In the case of using Haar mother wavelet transform, as shown in (a) inFIG. 8, V is the value of luminance difference between a pixel ofinterest 30, which is a pixel to be processed, and a right adjacentpixel 31, H is the value of luminance difference between the pixel ofinterest 30 and a bottom adjacent pixel 32, D is the value of luminancedifference between the pixel of interest 30 and a lower-right diagonallyadjacent pixel 33, and L is the average value of the luminance values ofthe above four pixels, namely, the pixel of interest 30, the rightadjacent pixel 31, the bottom adjacent pixel 32, and the lower-rightdiagonally adjacent pixel 33. In FIG. 8, (a) corresponds to scale 1, and(b) corresponds to scale 2. The image of scale 2 is an image in whicheach pixel has an average luminance value of four pixels in the image ofscale 1. In other words, the average luminance value of four pixels inthe image of scale 1, i.e., the output L, is the luminance value of eachblock of scale 2 for which the luminance difference value is calculated.An output V in scale 2 is the value of luminance difference between ablock 34 and a right adjacent block 35. An output H in scale 2 is thevalue of luminance difference between the block 34 and a bottom adjacentblock 36. An output D in scale 2 is the value of luminance differencebetween the block 34 and a lower-right adjacent block 37. The output Lin scale 2 is an average luminance value of the above four blocks,namely, the block 34, the right adjacent block 35, the bottom adjacentblock 36, and the lower-right adjacent block 37.

While the present embodiment describes the image feature quantityvectors using the wavelet coefficients, one or more exemplaryembodiments disclosed herein are not limited to this example, and anyimage feature quantities can be used. Examples of the image featurequantities include SIFT feature quantities, higher order localautocorrelation (HLAC) feature quantities, andhistograms-of-oriented-gradients (HOG) feature quantities.

The average normal structural vector g is obtained by calculating anaverage value for each element of the image feature quantity vectors.

The normal structural base vector matrix B is calculated throughprincipal component analysis as eigen vectors b₁, b₂, . . . , and b_(n)that are solutions of simultaneous equations given by Expression 3below.

$\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}{{Sb}_{1} = {\lambda_{1}b_{1}}} \\{{Sb}_{2} = {\lambda_{2}b_{2}}}\end{matrix} \\\vdots\end{matrix} \\{{Sb}_{n} = {\lambda_{n}b_{n}}}\end{matrix} & \lbrack {{Expression}\mspace{14mu} 3} \rbrack\end{matrix}$

Here, the matrix S represents a variance-covariance matrix and is givenby Expression 4 below.

$\begin{matrix}{S = \begin{pmatrix}S_{1}^{2} & {S_{1}S_{2}} & \ldots & {S_{1}S_{n}} \\{S_{1}S_{2}} & S_{2}^{2} & \ldots & {S_{2}S_{n}} \\\vdots & \vdots & \ddots & \vdots \\{S_{1}S_{n}} & {S_{2}S_{n}} & \ldots & S_{n}^{2}\end{pmatrix}} & \lbrack {{Expression}\mspace{14mu} 4} \rbrack\end{matrix}$

The eigen value λ is given by Expression 5 below.

$\begin{matrix}{\begin{matrix}{S_{1}^{2} - \lambda} & {S_{1}S_{2}} & \ldots & {S_{1}S_{n}} \\{S_{1}S_{2}} & {S_{2}^{2} - \lambda} & \ldots & {S_{2}S_{n}} \\\vdots & \vdots & \ddots & \vdots \\{S_{1}S_{n}} & {S_{2}S_{n}} & \ldots & {S_{n}^{2} - \lambda}\end{matrix}} & \lbrack {{Expression}\mspace{14mu} 5} \rbrack\end{matrix}$

It is assumed that n eigen values λ are obtained and are respectivelydenoted by λ₁, λ₂, . . . , and λ_(n) in descending order.

The above operations transform the image feature quantity vectors to abase representation and generate the normal image base coefficientvectors α.

The base vector transformation unit 105 is configured to transform thedirections of the base vectors of the test data 151 by matching thedirections with those of the base vectors stored in the normalcoefficient storage unit 104.

In the base representation, base coefficients for use in representingthe same data differ depending on the directions of base vectors. InExpression 5 in which the base vectors are rearranged in descendingorder, the base vectors are arranged in descending order of proportion.Thus, in the example of the distribution of data as shown in FIG. 9, afirst principal component 601 points in the direction of the maximumvariance as shown in (a) in FIG. 9. A second principal component 602 isorthogonal to the first principal component 601 and points in thedirection of the second maximum variance. On the other hand, it is alsopossible to obtain principal components having the same variance asshown in (b) in FIG. 9. Specifically, a first principal component 603and a second principal component 604 have similar widths with respect toan ellipse that represents the distribution of data.

When seen from a different point of view from that in FIG. 9, even ifthe method for calculating base vectors is fixed to Expression 5, thebase vectors to be calculated will vary depending on differentdistributions of data as shown in (a) and (b) in FIG. 10. In otherwords, if the distribution of data shown in (a) in FIG. 10, which is thesame as that shown in (a) in FIG. 9, changes to the distribution of dataas shown in (b) in FIG. 10, the principal components also change to afirst principal component 701 and a second principal component 702 thathave different directions.

From the above consideration, it can be found that even if thediagnostic support apparatus 100 and the image server 150 use the sametype of image feature quantities, their base vectors can vary dependingon the distribution of data. Since there is almost no possibility thattest images accumulated day by day in different facilities all match, itis reasonable to consider that the distributions of data do not matchdue to various factors such as different patients, different diseases,or different imaging devices. It is thus necessary to cope withdifferences in base vectors as shown in FIG. 10.

The transformation of base vectors can be implemented by replacing theeigen vectors b₁, b₂, . . . , and b_(n) of test data with those storedin the normal coefficient storage unit 104 and recalculating the basecoefficient vectors of the test data. In other words, the transformationof base vectors can be represented by the following expression:

$\begin{matrix}{\begin{bmatrix}\begin{matrix}{g_{r,1} + {b_{r,1,1}\alpha_{r,1}} +} \\{{b_{r,1,2}\alpha_{r,2}} + \ldots + {b_{r,1,n}\alpha_{r,n}}}\end{matrix} \\\begin{matrix}{g_{r,2} + {b_{r,2,1}\alpha_{r,1}} +} \\{{b_{r,2,2}\alpha_{r,2}} + \ldots + {b_{r,2,n}\alpha_{r,n}}}\end{matrix} \\\vdots \\\begin{matrix}{g_{r,n} + {b_{r,n,1}\alpha_{r,1}} +} \\{{b_{r,n,2}\alpha_{r,2}} + \ldots + {b_{r,n,n}\alpha_{r,n}}}\end{matrix}\end{bmatrix} = \lbrack \begin{matrix}\begin{matrix}{g_{s,1} + {b_{s,1,1}\alpha_{s,1}} +} \\{{b_{s,1,2}\alpha_{s,2}} + \ldots + {b_{s,1,n}\alpha_{s,n}}}\end{matrix} \\\begin{matrix}{g_{s,2} + {b_{s,2,1}\alpha_{s,1}} +} \\{{b_{s,2,2}\alpha_{s,2}} + \ldots + {b_{s,2,n}\alpha_{s,n}}}\end{matrix} \\\vdots \\\begin{matrix}{g_{s,n} + {b_{s,n,1}\alpha_{s,1}} +} \\{{b_{s,n,2}\alpha_{s,2}} + \ldots + {b_{s,n,n}\alpha_{s,n}}}\end{matrix}\end{matrix} \rbrack} & \lbrack {{Expression}\mspace{14mu} 6} \rbrack\end{matrix}$

Here, the left side represents test data represented by the normal imagebase vectors in the diagnostic support apparatus 100, with the suffix r.The right side represents test data represented by the base vectors inthe image server 150, with the suffix s. The base coefficient vectorsα_(r) on the left side are unknown. Thus, solving Expression 6 for thebase coefficient vectors α_(r) completes the transformation of basevectors. The base vector transformation unit 105 is configured to outputthe base coefficient vectors α_(r) as base coefficient vectorsrepresented with the same base vectors as those stored in the normalcoefficient storage unit 104.

The lesion determination unit 107 is configured to determine, for eachpixel in the test data, the presence or absence of a lesion site on thebasis of the distance between the base coefficient vector α_(r) and anormal image base coefficient vector α. In other words, the lesiondetermination unit 107 is configured to compare the distance between thebase coefficient vector α_(r) and the normal image base coefficientvector α with a determination threshold value. If the calculateddistance is greater than the determination threshold value, the lesiondetermination unit 107 determines that the pixel of interest from whichthe base coefficient vector α_(r) has been calculated is a pixel in alesion site, and if the distance is less than or equal to thedetermination threshold value, the lesion determination unit 107determines that the pixel of interest is a pixel in a normal site. Notethat the determination threshold value used to determine the presence orabsence of a lesion is calculated from past cases and is stored inadvance in the threshold value memory unit 108.

FIG. 11 shows an example of the method for determining the determinationthreshold value used to determine the presence or absence of a lesion.The determination threshold value is determined by adetermination-threshold-value deciding apparatus that includes an imagefeature quantity calculation unit 801, a base representation unit 802,the nearest neighbor vector detection unit 106, the normal coefficientstorage unit 104, and a vector distance calculation unit 803.

First, lesion images in which the presence of a lesion site has beenconfirmed are acquired from past cases and are classified by the diseasename. Next, the image feature quantity calculation unit 801 calculates,for example, a plurality of image feature quantities for each set ofimage coordinates (for each pixel) in lesion images I_(d) classifiedunder a disease name D and generates a lesion image feature quantityvector f_(d) (f_(d,2) to f_(d,na)) having the calculated image featurequantities as its vector elements. The lesion image feature quantityvector f_(d) is generated for each pixel. The base representation unit802 substitutes the lesion image feature quantity vector f_(d) into thevector f_(p) of Expression 2 to transform the lesion image featurequantity vector f_(d) to a vector α_(p). This vector α_(p) is referredto as a lesion image base coefficient vector α_(d). Then, the nearestneighbor vector detection unit 106 detects a normal image basecoefficient vector α that is most similar to the lesion coefficientvector α_(d) from among the normal image base coefficient vectors αstored in the normal coefficient storage unit 104. For example, a normalimage base coefficient vector α having the shortest distance from thelesion image base coefficient vector α_(d) is detected. The vectordistance calculation unit 803 calculates the distance between thedetected nearest neighbor normal image base coefficient vector α and thelesion image base coefficient vector α_(d) and determines the calculateddistance as a determination threshold value used to determine thepresence or absence of a lesion. It is, however, noted that since thereare a large number of lesion images classified under the same diseasename, it is more appropriate to, for example, determine an average ormedian value of determination threshold values calculated from eachpixel in each lesion site as the determination threshold value.

While, in FIG. 11, lesion images are classified by the disease name, itis also possible to calculate a determination threshold value fordetermining the condition of a disease if lesion images are classifiedby the condition of each disease, using, for example, findings attachedto the lesion images. For example, lesion images of tumors are dividedinto two categories, namely lesion images of benign tumors and lesionimages of malignant tumors, and a determination threshold value isobtained for each category of the lesion images. Using these twodetermination threshold values, the lesion determination unit 107 candetermine the condition of the disease as to whether the tumor is benignor malignant.

The determination result output unit 109 is configured to output theresult of determination by the lesion determination unit 107 to thedetermination result display unit 110. The determination result displayunit 110 is constituted by a display device or the like and isconfigured to receive the determination result from the determinationresult output unit 109, and if the result shows the presence of a lesionsite, replace the pixel values of the image coordinates in the lesionsite by a specific color (e.g., red or yellow), and display the presenceand position of the lesion site as an image.

The determination result output unit 109 is configured to output theresult of determination by the lesion determination unit 107.

The determination result display unit 110 is constituted by a displaydevice or the like and is configured to receive the determination resultfrom the determination result output unit 109, and if the result showsthe presence of a lesion site, replace the pixel values of the imagecoordinates in the lesion site by a specific color (e.g., red oryellow), and display the presence and position of the lesion site as animage.

FIG. 12 is a flowchart of processing performed by the diagnostic supportapparatus 100.

In step S90, prior to the processing for determining the presence orabsence of a lesion site, the diagnostic support apparatus 100 storesnormal image base coefficient vectors α into the normal coefficientstorage unit 104. Specifically, the diagnostic support apparatus 100calculates base vectors from image feature quantity vectors acquiredfrom a plurality of normal images and then generates the normal imagebase coefficient vectors α each having, as its vector elements,coefficients used to represent a normal image as a linear combination ofthese base vectors. The generated normal image base coefficient vectorsα are stored into the normal coefficient storage unit 104.

In step S91, the data reception unit 102 receives the test data 151 andthe image-feature-quantity management number 152 from the image server150. The test data 151 includes base vectors used as a basis torepresent the image feature quantities of a test image, and basecoefficient vectors.

In step S92, the image feature quantity comparison unit 103 compares theimage-feature-quantity management number 152 received by the datareception unit 102 with the image-feature-quantity management number ofthe image feature quantities corresponding to the coefficients stored inthe normal coefficient storage unit 104. Through this, the image featurequantity comparison unit 103 checks whether or not the image featurequantities of the test image match the image feature quantities of thenormal images. If they match, the procedure proceeds to step S93. Itthey do not match, the procedure proceeds to step S94, in which thecommunication control unit 101 notifies the image server 150 that theimage feature quantities of the test image do not match the imagefeature quantities of the normal images.

In step S93, the base vector transformation unit 105 replaces the basevectors of the test data with those stored in the normal coefficientstorage unit 104, using Expression 6, so as to calculate basecoefficient vectors α_(r).

In step S95, the nearest neighbor vector detection unit 106 detects anearest neighbor vector that is most similar to each base coefficientvector α_(r) with reference to the normal image base coefficients αstored in the normal coefficient storage unit 104.

In step S96, the lesion determination unit 107 calculates, for eachpixel in the test image, the distance between the test image basecoefficient vector α_(r) and the nearest neighbor normal image basecoefficient vector α and compares the calculated distance with thedetermination threshold value for use in determining the presence orabsence of a lesion.

If the distance between the base coefficient vector α_(r) and thenearest neighbor normal image base coefficient vector α is greater thanthe determination threshold value, the procedure proceeds to step S97.In step S97, the lesion determination unit 107 determines the “presenceof a lesion site” for the target pixel and outputs the determinationresult to the determination result output unit 109. Upon receipt of thedetermination result from the determination result output unit 109, thedetermination result display unit 110 sets a pixel value of, forexample, 1 (white) at the pixel position of the target pixel anddisplays a difference image that explicitly indicates the lesion site.

On the other hand, if the distance between the base coefficient vectorα_(r) and the nearest neighbor normal image base coefficient vector α isless than or equal to the determination threshold value, the procedureproceeds to step S98. In step S98, the lesion determination unit 107determines the “absence of a lesion site” for the target pixel andoutputs the determination result to the determination result output unit109. Upon receipt of the determination result from the determinationresult output unit 109, the determination result display unit 110 sets apixel value of 0 (black) at the pixel position of the target pixel anddisplays a difference image.

With the configuration described above, the diagnostic support apparatus100 can detect a lesion site from the difference between a test imageand normal images. According to the present embodiment, theimage-feature-quantity management number is used to determine whether ornot the image feature quantities of a test image match those of normalimages. If they match, the base vectors of the test data 151 aretransformed. Thus, even if different base vectors are used at respectivefacilities, the diagnostic support apparatus 100 can support image-baseddiagnosis among the facilities. In other words, it is possible toaccurately determine the presence or absence of a lesion site withoutdepending on the method for describing or calculating image featurequantities.

Medical images to be accumulated day by data at respective facilitiesare captured under various conditions such as using different types ofdiseases, different shapes of patients, different shooting sites, anddifferent settings for imaging devices. Even if the method forcalculating image feature quantities is standardized at respectivefacilities, image feature quantities do not match because image dataitself varies. The transformation of base vectors by the diagnosticsupport apparatus 100 resolves such mismatching of image featurequantities, which occurs as a matter of course, and makes it possible toshare normal image data among facilities.

Such a situation where various medical images are acquired can alsooccur when image data is divided into a plurality of pieces on a timeaxis. Specifically, in the case where image data is divided into animage data piece captured before time t and an image data piece capturedat and after time t, it is not practical to consider that both of theimage data pieces reconstitute exactly the same image data. Accordingly,a normal image generated by the image data piece captured before time tand a normal image generated by the image data piece captured at andafter time t cannot be associated with each other without transformationof base vectors.

The present embodiment makes it possible to support coordination amongvarious facilities, organizations, or professions such as coordinationbetween a university hospital and community hospitals or coordinationbetween different departments in the same hospital such as between anemergency visit and a clinical department. For example, if the imageserver 150 shown in FIG. 4 is installed in a community hospital and thediagnostic support apparatus 100 is installed in a university hospital,the support of coordination between these hospitals becomes possible byconnecting the diagnostic support apparatus 100 and the image server 150by the Internet.

Embodiment 2

The present embodiment describes a diagnostic support apparatus thatshares image-feature-quantity management numbers among a plurality offacilities and therefore can share the image feature quantities ofnormal images without depending on different methods for calculatingimage feature quantities.

FIG. 13 is a block diagram showing a functional configuration of adiagnostic support apparatus 1000 according to Embodiment 2. Thediagnostic support apparatus 1000 includes a communication control unit1001, the data reception unit 102, the image feature quantity comparisonunit 103, the normal coefficient storage unit 104, and a base vectortransformation unit 1005. Note that structural elements that are thesame as those in FIG. 4 are denoted by the same reference numerals and adetailed description thereof will be omitted.

The communication control unit 1001 is configured to notify an imageserver 1002 that the diagnostic support apparatus 1000 will receivenormal image base coefficient vectors 1003 and an image-feature-quantitymanagement number 152 from the image server 1002. The communicationcontrol unit 1001 is also configured to notify the data reception unit102 that the data reception unit 102 will receive the normal image basecoefficient vectors 1003 and the image-feature-quantity managementnumber 152. Note that the normal image base coefficient vectors 1003 arebase coefficient vectors with which the image feature quantities ofnormal images are transformed to a base representation.

While, in this operation, a communication control unit 1004 of the imageserver 1002 instructs a data transmission unit 154 to transmit thenormal image base coefficient vectors 1003 and theimage-feature-quantity management number 152, one or more exemplaryembodiments disclosed herein are not intended to limit the configurationand operations of the image server 1002.

The base vector transformation unit 1005 is configured to transform thedirections of base vectors of the image base coefficient vectors 1003 tothose of the base vectors stored in the normal coefficient storage unit104 and to output the resultant base coefficient vectors α_(r) to thenormal coefficient storage unit 104.

The processing described above allows the diagnostic support apparatus1000 to add normal image base coefficient vectors generated by imageservers 1002 installed in other facilities into the normal coefficientstorage unit 104. Since the diagnostic support apparatus 1000 determinesthe presence or absence of a lesion on the basis of a difference betweena test image and normal images, the performance in determining thepresence or absence of a lesion improves as the variety of normal imagesstored in the diagnostic support apparatus 100 increases. In addition,higher reliable diagnosis viewing the whole aspect is possible with agreater number of data pieces. The diagnostic support apparatus 1000 isthus capable of storing data in the normal coefficient storage unit 104without depending on different factors such as different patients'shapes and different image-capturing devices, thus improving thereliability of the lesion detection.

Note that the normal coefficient storage unit 104 storing updated dataaccording to Embodiment 2 is applicable to the diagnostic supportapparatus 100 described in Embodiment 1.

Embodiment 3

The present embodiment describes a diagnostic support apparatus thatshares image-feature-quantity management numbers among a plurality offacilities and therefore can detect a lesion site without depending ondifferent methods for calculating image feature quantities. Inparticular, the following describes an embodiment in which whendifferent methods are used to calculate the image feature quantities ofa test image and the image feature quantities of normal images, theimage feature quantities of the test image are once restored to thepixel values, and then the image feature quantities of the same type asthat of the normal images are re-calculated from the pixel values.

FIG. 14 is a block diagram showing a functional configuration of adiagnostic support apparatus 1100 according to Embodiment 3. Thediagnostic support apparatus 1100 includes the communication controlunit 101, an image feature quantity comparison unit 1101, a datareception unit 1102, a pixel value transformation unit 1103, an imagefeature quantity calculation unit 1104, a base representation unit 1105,the normal coefficient storage unit 104, the nearest neighbor vectordetection unit 106, the lesion determination unit 107, the thresholdvalue memory unit 108, the determination result output unit 109, and thedetermination result display unit 110. Note that structural elementsthat are the same as those in FIG. 4 are denoted by the same referencenumerals and a detailed description thereof will be omitted.

The image feature quantity comparison unit 1101 is configured to holdthe image-feature-quantity management number for the image featurequantities (coefficients) of normal images stored in the normalcoefficient storage unit 104 and to compare the image-feature-quantitymanagement number 152 received from the image server 150 with the storedimage-feature-quantity management number for matching. If theimage-feature-quantity management numbers match, the image featurequantity comparison unit 1101 instructs the data reception unit 1102 tooutput the test data 151 to the nearest neighbor vector detection unit106 through an output port B of the data reception unit 1102. Then, thedetection of a lesion is performed in the same manner as with thediagnostic support apparatus 100 of Embodiment 1. When theimage-feature-quantity management numbers do not match, the imagefeature quantity comparison unit 1101 instructs the data reception unit1102 to output the test data 151 to the pixel value transformation unit1103 through an output port A of the data reception unit 1102. Then, thepixel value transformation unit 1103 and the image feature quantitycalculation unit 1104 transform the image feature quantities of the testdata 151 to the image feature quantities of the same type as thosecorresponding to the base coefficient vectors stored in the normalcoefficient storage unit 104.

Specifically, the pixel value transformation unit 1103 is configured toperform inverse transformation of the transformation represented byExpression 2 on the base coefficient vectors α_(s) of the test data 151to restore the base coefficient vectors α_(s) to image featurequantities f_(s). The pixel value transformation unit 1103 is furtherconfigured to restore the image feature quantities f_(s) to pixelvalues. The processing for restoring the image feature quantities f_(s)to the pixel values can be performed using a known technique. Forexample, when wavelet transformation is used to calculate the imagefeature quantities f_(s), the image feature quantities f_(s) can berestored to the pixel values through inverse wavelet transformation.When landmarks are used to calculate the image feature quantities f_(s),the elements of the image feature quantity vectors correspond to thepixel values at the landmarks.

The image feature quantity calculation unit 1104 is configured totransform the pixel values obtained by the pixel value transformationunit 1103 to image feature quantities of the same type as thosecorresponding to the base coefficient vectors stored in the normalcoefficient storage unit 104. The above processing resolves mismatchingin image feature quantities between the base coefficient vectors α_(s)of the test data 151 received from the image server 150 and the basecoefficient vectors stored in the normal coefficient storage unit 104.

The base representation unit 1105 is configured to transform the imagefeature quantities f_(s) to the base coefficient vectors α_(s) usingExpression 2 and to output the base coefficient vectors α_(s) to thenearest neighbor vector detection unit 106.

The processing described above allows the diagnostic support apparatus1100 to detect a lesion by re-calculating the image feature quantitiesof a test image even if the image server 150 uses a different method forcalculating image feature quantities. This enables a user who wants tosend a request for diagnosis from one facility to another facility, torequest diagnosis by simply transmitting the image feature quantities ofa test image calculated in one facility to another facility withoutsecuring the matching of image feature qualities. Thus, the workflow ofmedical procedures can run smoothly. In other words, it is possible toaccurately determine the presence or absence of a lesion site withoutdepending on the method for describing or calculating image featurequantities.

Embodiment 4

The present embodiment describes a diagnostic support apparatus thatshares image-feature-quantity management numbers among a plurality offacilities and therefore can share the image feature quantities ofnormal images without depending on different methods for calculatingimage feature quantities. In particular, the following describes anembodiment in which, when different methods are used to calculate imagefeature quantities, the image feature quantities are once restored topixel values, and image feature quantities are re-calculated from thepixel values.

FIG. 15 is a block diagram showing a functional configuration of adiagnostic support apparatus 1200 according to Embodiment 4. Thediagnostic support apparatus 1200 includes a communication control unit1201, the image feature quantity comparison unit 1101, the datareception unit 1102, the pixel value transformation unit 1103, the imagefeature quantity calculation unit 1104, a base representation unit 1202,and the normal coefficient storage unit 104. Note that structuralelements that are the same as those in FIG. 13 or 14 are denoted by thesame reference numerals and a detailed description thereof will beomitted.

The communication control unit 1201 is configured to notify the imageserver 1002 that the diagnostic support apparatus 1200 will receive thenormal image base coefficient vectors 1003 and theimage-feature-quantity management number 152 from the image server 1002.The communication control unit 1201 is also configured to notify thedata reception unit 1102 that the data reception unit 1102 will receivethe normal image base coefficient vectors 1003 and theimage-feature-quantity management number 152.

The base representation unit 1202 is configured to transform imagefeature quantities f_(p) to base coefficient vectors α_(p) usingExpression 2 and to output the base coefficient vectors α_(p) to thenormal coefficient storage unit 104. Note that the image featurequantities f_(p) are image feature quantities of the same type as thosecorresponding to the base coefficient vectors stored in the normalcoefficient storage unit 104. The image feature quantities f_(p) arecalculated by the pixel value transformation unit 1103 and the imagefeature quantity calculation unit 1104.

The processing described above allows the diagnostic support apparatus1200 to add normal image base coefficient vectors generated by imageservers 1002 installed in other facilities into the normal coefficientstorage unit 104. In particular, even if the diagnostic supportapparatus 1200 and the image servers 1002 use different methods forcalculating image feature quantities, the normal image base coefficientvectors generated by the image servers 1002 can be added into the normalcoefficient storage unit 104. This allows normal images acquired in onefacility to be transformed to normal image base coefficient vectors thatcan also be used in other facilities, thus leading to an improvement inthe precision of diagnosis.

Note that the normal coefficient storage unit 104 storing updated dataaccording to Embodiment 4 is applicable to the diagnostic supportapparatus 1100 described in Embodiment 3.

Embodiment 5

The present embodiment describes a diagnostic support apparatus thatshares image-feature-quantity management numbers among a plurality offacilities and therefore can detect a lesion site without depending ondifferent methods for calculating image feature quantities. Inparticular, the following describes an embodiment in which, when eachfacility uses a different method for calculating image featurequantities, the method for calculating image feature quantities and basevectors are standardized among all facilities. The present embodimentdescribes an example in which the method for calculating image featurequantities and base vectors are standardized between an image server anda diagnostic support apparatus.

FIG. 16 is a block diagram showing a functional configuration of adiagnostic support apparatus 1300 according to Embodiment 5. Thediagnostic support apparatus 1300 includes the communication controlunit 101, an image feature quantity comparison unit 1301, a datareception unit 1302, the pixel value transformation unit 1103, the imagefeature quantity calculation unit 1104, an image feature quantitytransformation unit 1304, a base representation unit 1305, a normalcoefficient storage unit 1303, the nearest neighbor vector detectionunit 106, the lesion determination unit 107, the threshold value memoryunit 108, the determination result output unit 109, and thedetermination result display unit 110. Note that structural elementsthat are the same as those in FIG. 14 are denoted by the same referencenumerals and a detailed description thereof will be omitted.

The image feature quantity comparison unit 1301 is configured to holdthe image-feature-quantity management number for the image featurequantities (coefficients) of normal images stored in the normalcoefficient storage unit 1303 and to compare the image-feature-quantitymanagement number 152 received from the image server 150 with the storedimage-feature-quantity management number for matching.

If the image-feature-quantity management numbers match, the imagefeature quantity comparison unit 1301 instructs the data reception unit1302 to output the test data 151 to the nearest neighbor vectordetection unit 106 through an output port A of the data reception unit1302. Then, the diagnostic support apparatus 1300 detects a lesion inthe same manner as with the diagnostic support apparatus 100 ofEmbodiment 1.

If the image-feature-quantity management numbers do not match, the imagefeature quantity comparison unit 1301 instructs the data reception unit1302 to output the test data 151 to the pixel value transformation unit1103 through an output port B of the data reception unit 1302. Then, thepixel value transformation unit 1103 and the image feature quantitycalculation unit 1104 transform the image feature quantities of the testdata 151 to image feature quantities of the same type as thosecorresponding to the base coefficient vectors stored in the normalcoefficient storage unit 1303. The image feature quantity comparisonunit 1301 also instructs the normal coefficient storage unit 1303 tooutput the base coefficient vectors stored in the normal coefficientstorage unit 1303 to the image feature quantity transformation unit1304. In accordance with the instruction, the normal coefficient storageunit 1303 outputs the base coefficient vectors to the image featurequantity transformation unit 1304.

The image feature quantity transformation unit 1304 is configured toperform inverse transformation of the transformation represented byExpression 2 on the base coefficient vectors received from the normalcoefficient storage unit 1303 so as to transform the base coefficientvectors to image feature quantities, and to output the image featurequantities to the base representation unit 1305.

The base representation unit 1305 is configured to acquire the imagefeature quantities obtained by transforming the base coefficient vectorsreceived from the image server 150 and the image feature quantitiesobtained by transforming the base coefficient vectors stored in thenormal coefficient storage unit 1303. These two sets of image featurequantities are both calculated using the same calculation method. Thebase representation unit 1305 is configured to calculate basecoefficient vectors from each set of the image feature quantities, usingExpression 2. Accordingly, the base vectors are newly updated using bothof the data in the image server 150 and the data in the diagnosticsupport apparatus 1300.

Through the processing described above, the diagnostic support apparatus1300 re-calculates both of the base coefficient vectors included in thetest data 151 and the base coefficient vectors stored in the normalcoefficient storage unit 1303 to update the base vectors when thediagnostic support apparatus 1300 and the image server 150 uses adifferent method for calculating image feature quantities. This allowsthe diagnostic support apparatus 1300 to accurately detect a lesion. Inother words, it is possible to accurately determine the presence orabsence of a lesion site without depending on the method for describingor calculating image feature quantities.

Embodiment 6

The present embodiment describes a diagnostic support apparatus thatshares image-feature-quantity management numbers among a plurality offacilities and therefore can share the image feature quantities ofnormal images without depending on different methods for calculatingimage feature quantities. In particular, the following describes anembodiment in which, when facilities use different methods forcalculating image feature quantities, the method for calculating imagefeature quantities is standardized so that the base vectors can beshared among all facilities.

FIG. 17 is a block diagram showing a functional configuration of adiagnostic support apparatus 1400 according to Embodiment 6. Thediagnostic support apparatus 1400 includes a communication control unit1401, the image feature quantity comparison unit 1301, the datareception unit 1302, the pixel value transformation unit 1103, the imagefeature quantity calculation unit 1104, a base representation unit 1402,the normal coefficient storage unit 1303, and the image feature quantitytransformation unit 1304. Note that structural elements that are thesame as those in FIG. 14, 15, or 16 are denoted by the same referencenumerals and a detailed description thereof will be omitted.

The communication control unit 1401 is configured to notify the imageserver 1002 that the diagnostic support apparatus 1400 will receive thenormal image base coefficient vectors 1003 and theimage-feature-quantity management number 152 from the image server 1002.The communication control unit 1401 is also configured to notify thedata reception unit 1302 that the data reception unit 1302 will receivethe normal image base coefficient vectors 1003 and theimage-feature-quantity management number 152.

The base representation unit 1402 is configured to transform imagefeature quantities f_(p) to base coefficient vectors α_(p) usingExpression 2 and to output the base coefficient vectors α_(p) to thenormal coefficient storage unit 1303. Note that the image featurequantities f_(p) are either the image feature quantities output from theimage feature quantity calculation unit 1104 or the image featurequantities output from the image feature quantity transformation unit1304. The image feature quantities output from the image featurequantity calculation unit 1104 are the image feature quantitiescalculated from the normal image base coefficient vectors 1003 using thesame calculation method as that used to calculate the image featurequantities corresponding to the base coefficient vectors stored in thenormal coefficient storage unit 1303. The image feature quantitiesoutput from the image feature quantity transformation unit 1304 are theimage feature quantities transformed from the base coefficient vectorsstored in the normal coefficient storage unit 1303.

The processing described above allows the diagnostic support apparatus1400 to add normal image base coefficient vectors generated by imageservers 1002 installed in other facilities into the normal coefficientstorage unit 1303. In particular, even if the diagnostic supportapparatus 1400 and the image servers 1002 use different methods forcalculating image feature quantities, the normal image base coefficientvectors generated by the image servers 1002 can be added into the normalcoefficient storage unit 1303. This allows the diagnostic supportapparatus 1400 to support the detection of a lesion without depending ondifferent factors such as different patient shapes, different shootingdevices, and different shooting conditions.

Note that the normal coefficient storage unit 1303 storing updated dataaccording to Embodiment 6 is applicable to the diagnostic supportapparatus 1300 described in Embodiment 5.

As described above, the diagnostic support apparatuses according toEmbodiments 1 to 6 described above can support the detection of a lesionwithout depending on different methods for calculating image featurequantities and any difference in data of normal images. In addition, thediagnostic support apparatus can stably and efficiently generate normalimages. In other words, the processing for comparing a test image withnormal images based on the memories of image-based diagnosticians can beexecuted by a computer. This makes it possible to objectively supportdiagnosis by image-based diagnosticians. Moreover, the results ofdiagnosis can be used in various scenes such as in the scene of informedconsent and in the scenes of medical education and basic medicine.

It is also possible to efficiently and accurately detect a lesion site.Image-based diagnosticians make new tests while referencing past casedata that is being updated day by day. Thus, the detection of a lesionaided with a computer improves the work efficiency of image-baseddiagnosticians and allows the medical workflow to run smoothly. Sincethe confirmation of a disease name and the condition of the disease hassignificant influences on the determination of a treatment plan,diagnostic support by the diagnostic support apparatuses can contributegreatly to improvements in the efficiency and quality of the entiremedical field.

The structural elements in the above-described embodiments may beconfigured in the form of an exclusive hardware product or may beimplemented by executing a software program suitable for the structuralelements. The structural elements may be implemented by a programexecuting unit such as a CPU or a processor reading and executing asoftware program recorded on the hard disk or a recording medium such asa semiconductor memory. Note here that the software program implementingthe diagnostic support apparatuses of the above-described embodiments isas described below.

Specifically, the software program causes the computer to execute a basevector matching step, a lesion determination step, and a determinationresult output step. The base vector matching step is of matching basevectors and base vectors, the base vectors being different from the basevectors, the base vectors being used as a basis to represent a testfeature quantity that is an image feature quantity of a test image inwhich presence of an image of a lesion site is unknown, and the basevectors being used as a basis to represent a normal feature quantitythat is an image feature quantity of a normal image that does notinclude an image of a lesion site. The lesion determination step is ofdetermining that the test image includes an image of a lesion site whena difference between a coefficient and a coefficient is greater than adetermination threshold value, the coefficient being a coefficient withwhich the test feature quantity is transformed to a base representation,and the coefficient being a coefficient with which the normal featurequantity is transformed to a base representation. The determinationresult output step is of outputting the determination result obtained inthe lesion determination step.

Note that the standardization of the base vectors among a plurality offacilities according to one or more exemplary embodiments eliminates theneed to transmit base vectors among the facilities. Thus, the data ofonly base coefficient vectors are exchanged among facilities. The basecoefficient vectors function only in combination with the base vectorsand thus have no meaning by themselves. From this, exchanging only thebase coefficient vectors among facilities has the advantage in terms ofinformation security that even if the third party acquires the basecoefficient vectors exchanged among facilities, the base coefficientvectors have no use value.

Consider a case where image data in a facility A is transmitted to afacility B and base vectors are calculated in the facility B, using bothof the image data in the facility A and image data in the facility B.The facility B holds base vectors that are common to the image data inthe facility A and the image data in the facility B, but at this time,the facility A does not hold these base vectors. The common base vectorscalculated in the facility B may be transmitted as they are to thefacility A, but this is too risky in terms of security because if thesebase vectors are once known by the third party, it is possible for thethird party to restore original images, using subsequently acquired basecoefficient vectors. In view of this, it is conceivable to divide asignal string that represents base vectors into a plurality of piecesand transmit these pieces at different times, as shown in (a) in FIG.18. This reduces the risk of information leakage.

Specifically, a signal string 1701 transmitted at time T1 from thefacility B to the facility A consists of an ID number 1702 and a datastring 1703. A signal string 1704 transmitted at time T2 from thefacility B to the facility A consists of an ID number 1705 and a datastring 1706. That is, the signal strings 1701 and 1704 are transmittedat different times T1 and T2 on a network. The facility B furthertransmits a signal string 1707 to the facility A at time T3, the signalstring 1707 including concatenation information indicating aconcatenation of the signal strings 1701 and 1704. The facility A readsthe signal string 1707 and acquires the concatenation informationindicated by the signal string 1707. In the example shown in (a) in FIG.18, the ID numbers “101” and “323” are read from the signal string 1707as the concatenation information. In accordance with this concatenationinformation, the facility A selects the signal string 1701 having the IDnumber 1702 of “101” and the signal string 1704 having the ID number1705 of “323” in this order. The facility A also combines the datastring 1703 included in the signal string 1701 and the data string 1706included in the signal string 1704 in this order as shown in part (b) inFIG. 18. For example, the signal strings 1701, 1704, and 1707 aretransmitted from the data transmission unit 154 of the image server 150to the data reception unit 102 of the diagnostic support apparatus 100.The data reception unit 102 restores the base vectors of the imagefeature quantities of a test image in the same manner as described aboveon the basis of the signal strings 1701, 1704, and 1707.

Note that the base vectors of the image feature quantities of normalimages may be updated when, for example, a disease name that has not yetbeen registered has been received with reference to the classificationof disease names shown in FIG. 11. Alternatively, the base vectors maybe updated when a difference between a test image base coefficientvector and the detected nearest neighbor normal image base coefficientvector exceeds a reference value. Specifically, the diagnostic supportapparatus 100 shown in FIG. 4 may be replaced with a diagnostic supportapparatus 1804 shown in FIG. 19. The diagnostic support apparatus 1804differs from the diagnostic support apparatus 100 in configuration thatthe communication control unit 101 and the nearest neighbor vectordetection unit 106 are replaced respectively by a communication controlunit 1803 and a nearest neighbor vector detection unit 1801, and anearest neighbor detection monitoring unit 1802 is additionallyprovided. The communication control unit 1803 and the nearest neighborvector detection unit 1801, which respectively have the same functionsas the communication control unit 101 and the nearest neighbor vectordetection unit 106, further have additional functions described below.The nearest neighbor vector detection unit 1801 is configured to, when anearest neighbor vector has been detected, output a difference betweenthe normal image base coefficient vector and the test image basecoefficient vector to the nearest neighbor detection monitoring unit1802. The nearest neighbor detection monitoring unit 1802 is configuredto, when the difference between the normal image base coefficient vectorand the test image base coefficient vector exceeds a reference value,determine that an unexpected test image has been received and instructthe communication control unit 1803 to update the base vectors. Thecommunication control unit 1803 is configured to perform control forupdating the base vectors.

The diagnostic support apparatus 100 according to Embodiment 1 isconfigured to determine the presence or absence of a lesion site onlywhen the same method is used to calculate the image feature quantitiesof a test image and the image feature quantities of normal images. Inaddition to this, the diagnostic support apparatus 100 may be configuredto, when different methods are used to calculate the image featurequantities of a test image and the image feature quantities of normalimages, determine the presence or absence of a lesion site by executingthe same processing as that performed by the diagnostic supportapparatus 1100 described in Embodiment 3 or the diagnostic supportapparatus 1300 described in Embodiment 5.

While the above has been a description of the diagnostic supportapparatuses according to one or more exemplary embodiments, theinventive concept is not limited to these exemplary embodiments. Thoseskilled in the art will readily appreciate that various modificationsmay be made in the exemplary embodiments, and other embodiments may bemade by arbitrarily combining some of the structural elements ofdifferent exemplary embodiments without departing from the principlesand spirit of the inventive concept.

The subject matter disclosed herein is to be considered descriptive andillustrative only, and the appended Claims are of a scope intended tocover and encompass not only the particular embodiments disclosed, butalso equivalent structures, methods, and/or uses.

INDUSTRIAL APPLICABILITY

One or more exemplary embodiments disclosed herein are applicable to,for example, diagnostic support apparatuses that specify lesion sitesfrom medical images to thereby support image-based diagnosis by doctors.

1. A diagnostic support apparatus comprising: a base vector matchingunit configured to match base vectors and base vectors, the base vectorsbeing different from the base vectors, the base vectors being used as abasis to represent a test feature quantity that is an image featurequantity of a test image in which presence of an image of a lesion siteis unknown, and the base vectors being used as a basis to represent anormal feature quantity that is an image feature quantity of a normalimage that does not include an image of a lesion site; a lesiondetermination unit configured to determine that the test image includesan image of a lesion site when a difference between a coefficient and acoefficient is greater than a determination threshold value, thecoefficient being a coefficient with which the test feature quantity istransformed to a base representation, and the coefficient being acoefficient with which the normal feature quantity is transformed to abase representation; and a determination result output unit configuredto output a result of the determination by the lesion determinationunit.
 2. The diagnostic support apparatus according to claim 1, whereinthe base vector matching unit includes a base vector transformation unitconfigured to, when the test feature quantity and the normal featurequantity match in type, perform transformation processing for matchingthe base vectors used as a basis to represent the test feature quantityto the base vectors used as a basis to represent the normal featurequantity, and represent the test feature quantity as a linearcombination of the base vectors of the normal feature quantity with thecoefficient, and the lesion determination unit is configured todetermine that the test image includes an image of a lesion site whenthe difference between the coefficient with which the normal featurequantity is transformed to a base representation and the coefficientused with the test image base vectors that have undergone thetransformation processing is greater than the determination thresholdvalue.
 3. The diagnostic support apparatus according to claim 2, furthercomprising a normal coefficient storage unit configured to store atleast one of the coefficient with which the normal feature quantity istransformed to a base representation, wherein the lesion determinationunit is configured to determine that the test image includes an image ofa lesion site when a difference between a selected one of the at leastone coefficient stored in the normal coefficient storage unit and thecoefficient used with the test image base vectors that have undergonethe transformation processing is greater than the determinationthreshold value.
 4. The diagnostic support apparatus according to claim3, wherein the base vector transformation unit is further configured toreceive a first coefficient from an external apparatus, the firstcoefficient being a coefficient with which an image feature quantity ofa normal image is transformed to a base representation, the imagefeature quantity being of the same type as the normal feature quantity,to perform transformation processing for matching first base vectorscorresponding to the first coefficient with second base vectorscorresponding to the at least one coefficient stored in the normalcoefficient storage unit, to represent the image feature quantitycorresponding to the first coefficient as a linear combination of thesecond base vectors with a second coefficient, and to add the secondcoefficient into the normal coefficient storage unit.
 5. The diagnosticsupport apparatus according to claim 2, further comprising a datareception unit configured to receive first data and second data atdifferent times and combine the received first data and the receivedsecond data to restore the base vectors used as a basis to represent thetest feature quantity, the first data and the second data being obtainedby dividing the base vectors of the test feature quantity.
 6. Thediagnostic support apparatus according to claim 2, further comprising anearest neighbor detection monitoring unit configured to output aninstruction to update the base vectors of the normal feature quantitywhen the difference between the coefficient with which the normalfeature quantity is transformed to a base representation and thecoefficient with which the test feature quantity is transformed to abase representation is greater than a reference value.
 7. The diagnosticsupport apparatus according to claim 1, wherein the base vector matchingunit includes: a pixel value transformation unit configured to, when thetest feature quantity and the normal feature quantity do not match intype, transform the test feature quantity to a pixel value to restorethe test image; an image feature quantity calculation unit configured tocalculate an image feature quantity of the same type as the normalfeature quantity from the restored test image; and a base representationunit configured to represent the image feature quantity calculated bythe image feature quantity calculation unit as a linear combination ofthe base vectors, with which the normal feature quantity is transformedto a base representation, with a coefficient, and the lesiondetermination unit is configured to determine that the test imageincludes an image of a lesion site when a difference between thecoefficient with which the normal feature quantity is transformed to abase representation and the coefficient used by the base representationunit to represent the calculated image feature quantity is greater thanthe determination threshold value.
 8. The diagnostic support apparatusaccording to claim 7, further comprising a normal coefficient storageunit configured to store at least one of the coefficient with which thenormal feature quantity is transformed to a base representation, whereinthe pixel value transformation unit is further configured to receive animage feature quantity of a normal image from an external apparatus andtransform the received image feature quantity to a pixel value torestore the normal image, the image feature quantity being of adifferent type from the normal feature quantity, the image featurequantity calculation unit is further configured to calculate an imagefeature quantity of the same type as the normal feature quantity fromthe restored normal image, and the base representation unit is furtherconfigured to represent the image feature quantity calculated by theimage feature quantity calculation unit as a linear combination of thebase vectors, with which the normal feature quantity is transformed to abase representation, with a coefficient, and to add the coefficient intothe normal coefficient storage unit.
 9. The diagnostic support apparatusaccording to claim 7, further comprising an image feature quantitytransformation unit configured to transform the coefficient, with whichthe normal feature quantity is transformed to a base representation, tothe normal feature quantity, wherein the base representation unit isconfigured to calculate base vectors from the normal feature quantitytransformed by the image feature quantity transformation unit and theimage feature quantity calculated by the image feature quantitycalculation unit, and to represent each of the transformed normalfeature quantity and the calculated image feature quantity as a linearcombination of the calculated base vectors with a coefficient.
 10. Thediagnostic support apparatus according to claim 9, further comprising anormal coefficient storage unit configured to store at least one of thecoefficient with which the normal feature quantity is transformed to abase representation, wherein the pixel value transformation unit isfurther configured to receive an image feature quantity of a normalimage from an external apparatus and transform the received imagefeature quantity to a pixel value to restore the normal image, the imagefeature quantity being of a different type from the normal featurequantity, the image feature quantity calculation unit is furtherconfigured to calculate an image feature quantity of the same type asthe normal feature quantity from the restored normal image, and the baserepresentation unit is further configured to calculate base vectors fromthe normal feature quantity transformed by the image feature quantitytransformation unit and the image feature quantity calculated by theimage feature quantity calculation unit, to represent the calculatedimage feature quantity as a linear combination of the calculated basevectors with a coefficient, and to add the coefficient into the normalcoefficient storage unit.
 11. A diagnostic support method executed by acomputer, the method comprising: matching base vectors and base vectors,the base vectors being different from the base vectors, the base vectorsbeing used as a basis to represent a test feature quantity that is animage feature quantity of a test image in which presence of an image ofa lesion site is unknown, and the base vectors being used as a basis torepresent a normal feature quantity that is an image feature quantity ofa normal image that does not include an image of a lesion site;determining that the test image includes an image of a lesion site whena difference between a coefficient and a coefficient is greater than adetermination threshold value, the coefficient being a coefficient withwhich the test feature quantity is transformed to a base representation,and the coefficient being a coefficient with which the normal featurequantity is transformed to a base representation; and outputting aresult of the determination by the lesion determination unit.
 12. Anon-transitory computer-readable recording medium that stores a programfor causing a computer to perform the diagnostic support methodaccording to claim 11.